Various systems have been proposed for sensing the interior volume of freight cars and trailers for cargo, and for transmitting information gleaned from the sensors to a remote site such as an asset management center. See, for example, the U.S. Pat. No. 6,437,702 to Ragland et al., the U.S. Pat. No. 6,919,803 to Breed and the U.S. Patent Application Publication No. 2004/0140886 to Cleveland et al. A system disclosed in the Breed patent, for example, includes one or more imagers and a pattern recognition neural network for extracting cargo-related data for transmission to a remote location.
Although it is theoretically possible to glean detailed cargo information through the use of sophisticated processing techniques such as neural networks, it is fundamentally essential that the system be capable of reliably distinguishing between an empty container and a non-empty container. Also, it is important to minimize system cost, and to operate with low power consumption since the available electrical power may be limited to an on-board battery. Accordingly, what is needed is a cost-effective vision-based cargo sensing method that reliably and efficiently determines the empty vs. non-empty status of a cargo container.